Method and apparatus for dynamically allocating power in a data center

ABSTRACT

Embodiments of the invention relate generally to the field of power management of computer systems, and more particularly to a method and apparatus for dynamically allocating power to servers in a server rack. The method comprises: measuring power consumption of a computer system having one or more servers; estimating probability distribution of power demand for each of the one or more servers, the estimation based on the measured power consumption; estimating performance loss via the estimated probability distribution; computing power capping limits for each of the one or more servers, the computation based on the estimated probability distribution and the performance loss; and dynamically allocating the power capping limits to each of the one or more servers by modifying previous power capping limits of each of the one or more servers.

CLAIM OF PRIORITY

The present application is a Continuation of, and claims priority to, and incorporates by reference in its entirety the corresponding U.S. patent application Ser. No. 12/637,591 filed on Dec. 14, 2009, and entitled “METHOD AND APPARATUS FOR DYNAMICALLY ALLOCATING POWER IN A DATA CENTER.”

FIELD OF THE INVENTION

Embodiments of the invention relate generally to the field of power management of computer systems, and more particularly to a method and apparatus for dynamically allocating power capping limits to servers in a server rack.

BACKGROUND

A server rack is designed for a particular power consumption envelope that depends on factors such as, number of servers in the server rack, type of servers in the server rack (e.g., servers with low power CPU(s) or high power CPU(s)), cooling system of the room housing the server rack, power supply distribution network in the server rack for the servers, etc. Servers in a computer system, such as a server rack, execute a number of applications and may have a diverse workload. Diverse workload means that a server in a computer system may not consume the same amount of power as another server, at a given time, in the same rack because of different workloads that require different processor utilization. A fully utilized processor in a server means that no processing cycles of the processors are wasted.

However, servers may not be fully utilized because of the power limit placed on the servers by the total power capacity of the server rack. Such underutilized servers caused by power limiting in the server rack may exhibit performance loss. Performance loss is defined as processor utilization that would have occurred had the processor been allowed to process without any power consumption limit. The power limit placed on the servers may also result from an internal power limit set for the server by the server itself. For example, a power controller unit in the server may set the server power capacity to a conservative limit based on processor reliability and longevity benchmarks. If the processor (or the server housing the processor) tends to consume power above the conservative limit (generally monitored via thermal sensors in, on, or around the processor), then the processor throttles. Throttling means that the processor operating frequency and/or power supply level is reduced to lower the power consumed as well as the heat generated by the processor.

One way to increase the computational capabilities of the servers and to also reduce performance loss of the servers in the server rack is to provide better temperature cooling facilities to the server rack combined with raising the power limit set by the power controller unit of each server. However, such a method for reducing performance loss does not take into account the power consumption of individual servers based on their workload. Such a method also requires physical infrastructure changes such as better temperature cooling facilities and redesign of power distribution network in the servers within the server rack. Furthermore, heuristic approaches that determine power budgets for individual servers in a server rack use an ad-hoc power assignment methodology that do not take into account the foreseeable power demand of servers in view of their performance loss.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the invention will be understood more fully from the detailed description given below and from the accompanying drawings of various embodiments of the invention, which, however, should not be taken to limit the invention to the specific embodiments, but are for explanation and understanding only.

FIG. 1 is a flow chart for computing power capping limits by computing probability distribution of power demand, according to one embodiment of the invention.

FIG. 2A is a box graph showing power demand for servers in a server rack before applying the dynamically allocated power capping limits from the dynamic allocator to the servers.

FIG. 2B is a box graph showing power demand for servers in a server rack after applying the dynamically allocated power capping limits from the dynamic allocator to the servers, according to one embodiment of the invention.

FIG. 3 is a table showing the relative reduction in performance loss via the dynamic power allocator, according to one embodiment of the invention.

FIG. 4 illustrates an apparatus having a server rack coupled with a dynamic power allocator, according to one embodiment of the invention.

FIG. 5 illustrates an apparatus for dynamically allocating power capping limit to a server, according to one embodiment of the invention.

DETAILED DESCRIPTION

Embodiments of the invention relate to a method and apparatus for dynamically allocating power capping limits to servers in a server rack. In one embodiment, actual power consumption of each server in the server rack is monitored at regular intervals and a power demand is estimated based on computing a probability distribution of the power demand and estimated performance loss of each server in the server rack. In one embodiment, new power capping limits for each server in the server rack is estimated iteratively and dynamically allocated to the server to achieve reduction in the server performance loss.

Reference in the specification to “an embodiment,” “one embodiment,” “some embodiments,” or “other embodiments” means that a particular feature, structure, or characteristic described in connection with the embodiments is included in at least some embodiments, but not necessarily all embodiments. The various appearances of “an embodiment,” “one embodiment,” or “some embodiments” are not necessarily all referring to the same embodiments. If the specification states a component, feature, structure, or characteristic “may,” “might,” or “could” be included, that particular component, feature, structure, or characteristic is not required to be included. If the specification or claim refers to “a” or “an” element, that does not mean there is only one of the element. If the specification or claims refer to an “additional” element, that does not preclude there being more than one of the additional element.

FIG. 1 illustrates a flow chart 100 for computing power capping limits by computing a probability distribution of power demand of servers in a computer system, according to one embodiment of the invention. At block 101 a total power limit of the computer system is determined. In one embodiment, the computer system is a server rack having one or more servers stored in the rack. In one embodiment, the total power limit of the computer system is based on the total power capacity of the computer system for a particular power distribution network and the temperature cooling system for the computer system.

At block 102, power consumption of the computer system is measured. In one embodiment, the measuring is performed via a power controller in each server. In one embodiment, the power controller provides the current power consumption of the server based on the amount of current provided to the processor (or multiple processors) in the server at a given power supply voltage level. The power controller, in one embodiment, also communicates new power capping limits to the processor (or multiple processors) in the server. In one embodiment, the power controller on the server reads the power consumption directly from power supply unit of the server, and then throttles the CPU(s) with a feedback control loop if a power consumption and/or temperature threshold is crossed. In such an embodiment, the power controller does not require knowledge of the power consumption of the CPU(s) for monitoring and controlling the power consumption of the server.

In one embodiment, the measured power consumption of every server (ρ₁ ^((t)), . . . , ρ_(n) ^((t))) in the computer system and the determined power limit of the computer system are provided to the dynamic power allocator. In one embodiment, the dynamic power allocator is situated at a remote location and is configured to compute, based on computing a probability distribution of power demand and estimated performance loss for each server, a power capping limit for each server.

At block 103, the probability distribution of power demand for all servers (one or more) is estimated. The probability distribution models the behavior of power demand of each server in the computer system at every time step t. In one embodiment, the time step t is configurable by a user or another computing machine. In one embodiment, the time step t is 30 seconds. In one embodiment, the power demand of a server is the power consumption that sustains the server workload without power capping.

The mathematical expressions discussed herein are for illustration purposes. Embodiments of the invention are not limited to these mathematical expressions.

At block 104, performance loss of each server in the computer system is estimated. Performance loss is defined as processor utilization that would have occurred had the processor been allowed to process without any power capping limit. In one embodiment, performance loss of a server configured to operate under a power capping limit is positively correlated with a gap between the power demand and the power capping limit. The power capping limit of a server is the upper limit for power consumption of a server—server processor(s) is/are throttled near or at the power capping limit. In one embodiment, the instant at which a server is throttled (including the CPU(s) in the server) is the power capping limit of the server.

In one embodiment, the probability distribution of power demand for all servers in a computer system at time step t is expressed as:

P(D _(i) ^((t)) =d _(i) ^((t)))

where D_(i) ^((t)) denotes the random variable of the power demand at time step t, where d_(i) ^((t)) denotes the values of the random variable of the power demand, and where ‘i’ ranges from 1 to n number of servers in a computer system.

In one embodiment, performance loss of each server in the computer system is computed via an expectation of gaps (differences) between the power demand (D_(i) ^((t))) and the power capping limit (c_(i) ^((t))) of each server with respect to the probability distribution P(D_(i) ^((t))=d_(i) ^((t))). In one embodiment, the gap between the power demand (D_(i) ^((t))) and the power capping limit (c_(i) ^((t))) of each server is expressed as:

D _(i) ^((t)) −c _(i) ^((t)) for d _(i) ^((t)) >c _(i) ^((t))(i=1, . . . , n)

In one embodiment, a demand bigram model and a power capping model is used to model the performance loss of a server in the computer system. A demand bigram model, in one embodiment, can be expressed as P(d_(i) ^((t))|d_(i) ^((t−1))). For the sake of not obscuring the embodiments of the invention, it is assumed that the power demand of a server in the current time step t is highly correlated with the power demand in the previous time step t-1. Such high correlation at various time steps results in a first-order Markov chain. In other embodiments, the power demand of a server in the current time step t depends on more information other than the power demand in the previous time step t-1. For example, in one embodiment, more information includes power demand values of several previous time steps to predict whether there would be a rise in power demand in the next time step. In such an embodiment higher order Markov chains may be needed to estimate performance loss of a server.

In one embodiment, the demand bigram model assigns a higher probability (i.e., higher than the mean value) in estimating the performance loss of a server (discussed later) if the current time step power demand d_(i) ^((t)) is close in value to the previous time step power demand d_(i) ^((t−1)). In one embodiment, if the current time step power demand d_(i) ^((t)) is not close in value to the previous time step power demand d_(i) ^((t−1)) then a lower probability (i.e., lower than the mean value) is assigned by the demand bigram model in estimating the performance loss (discussed later) of the server. In one embodiment, the probability distribution of the power demand is expressed as a Gaussian distribution with mean as d_(i) ^((t−1)).

In one embodiment, if the power demand of a server is lower than the power capping limit of the server, then the resulting power consumption of the server will be proximate in value to the value of the power demand. In one embodiment, if the power demand of a server is higher than the power capping limit of the server, then the resulting power consumption of the server is proximate to the value of the power capping limit of the server.

Based on the above two embodiments, the probability distribution of power consumption of a server can be expressed by the following probability model:

P(ρ_(i) ^((t)) |d _(i) ^((t)) ,c _(i) ^((t)))

In one embodiment, power capping model is used for estimating performance loss of a server. An example of a power capping model can be mathematically expressed as:

${{{If}\mspace{14mu} d} < {c - \delta}},{{P\left( {\left. \rho \middle| d \right.,c} \right)} = \left\{ {{{\begin{matrix} {1,} & {\rho = d} \\ {0,} & {\rho \neq d} \end{matrix}{If}\mspace{14mu} d} \geq {c - \delta}},{{P\left( {\left. \rho \middle| d \right.,c} \right)} = \left\{ {{{\begin{matrix} {\frac{1}{{2\delta} + 1},} & {p \geq {d - {\delta \mspace{14mu} {and}\mspace{14mu} \rho}} \leq {d + \delta}} \\ {0,} & {p < {d - {\delta \mspace{14mu} {or}\mspace{14mu} \rho}} > {d + \delta}} \end{matrix}{If}\mspace{14mu} d} > c},{{P\left( {\left. \rho \middle| d \right.,c} \right)} = \left\{ \begin{matrix} {{\left( {1 - \beta} \right)\frac{1}{{2\delta} + 1}},} & {\rho \geq {c - {\delta \mspace{14mu} {and}\mspace{14mu} \rho}} \leq {c + \delta}} \\ {0,} & {\rho < {c - \delta}} \\ {{\beta \frac{1}{c_{\max} - c - \delta}},} & {p > {c + \delta}} \end{matrix} \right.}} \right.}} \right.}$

where d is the power demand of a server, c is the power capping limit of the server, ρ is the probability distribution of the server power demand, δ is a small number (e.g., 0.1) to characterize possible fluctuation in the power capping limit of the server, β is a smoothing parameter having a small value (e.g., 0.1) to characterize possible effects of failure in capping the power consumption of the server, and where C_(max) is the maximum allowed value of c. The above expressions illustrate that if the power demand of the server is far below the power capping limit of the server then the power consumption of the server will equal to the power demand of the server, and if the power demand of the serve is close to or larger than the power capping limit of the server then the power consumption of the server will fluctuate around the power capping limit of the server.

In one embodiment, a Bayesian Theorem is applied to estimate/compute the probability distribution of the power demand and/or to estimate the performance loss of a server. In one embodiment, the Bayesian Theorem uses the demand bigram model and the power capping model along with the power consumption history of the server at every time step to compute the probability distribution of the power demand of the server.

In one embodiment, an iterative method is used for estimating the probability distribution of the power demand of the server in view of performance loss of the server. Such an iterative method, in one embodiment, can be mathematically expressed as:

h_(i)^((t)) = (ρ_(i)^((t − 1)), c_(i)^((t − 1)), h_(i)^((t − 1))) ${\hat{P}\left( d_{i}^{({t - 1})} \middle| h_{i}^{(t)} \right)} = {{\hat{P}\left( {\left. d_{i}^{({t - 1})} \middle| \rho_{i}^{({t - 1})} \right.,c_{i}^{({t - 1})},h_{i}^{({t - 1})}} \right)} = \frac{{P\left( {\left. \rho_{i}^{({t - 1})} \middle| d_{i}^{({t - 1})} \right.,c_{i}^{({t - 1})}} \right)}{\hat{P}\left( d_{i}^{({t - 1})} \middle| h_{i}^{({t - 1})} \right)}}{\underset{d}{\Sigma}{P\left( {\left. \rho_{i}^{({t - 1})} \middle| d \right.,c_{i}^{({t - 1})}} \right)}{\hat{P}\left( d \middle| h_{i}^{({t - 1})} \right)}}}$ $\begin{matrix} {{\hat{P}\left( d_{i}^{(t)} \middle| h_{i}^{(t)} \right)} = {\underset{d_{i}^{({t - 1})}}{\Sigma}{P\left( d_{i}^{(t)} \middle| d_{i}^{({t - 1})} \right)}{\hat{P}\left( {\left. d_{i}^{({t - 1})} \middle| \rho_{i}^{({t - 1})} \right.,c_{i}^{({t - 1})},h_{i}^{({t - 1})}} \right)}}} \\ {= {\underset{d_{i}^{({t - 1})}}{\Sigma}{P\left( d_{i}^{(t)} \middle| d_{i}^{({t - 1})} \right)}\frac{{P\left( {\left. \rho_{i}^{({t - 1})} \middle| d_{i}^{({t - 1})} \right.,c_{i}^{({t - 1})}} \right)}{\hat{P}\left( d_{i}^{({t - 1})} \middle| h_{i}^{({t - 1})} \right)}}{\underset{d}{\Sigma}{P\left( {\left. \rho_{i}^{({t - 1})} \middle| d \right.,c_{i}^{({t - 1})}} \right)}{\hat{P}\left( d \middle| h_{i}^{({t - 1})} \right)}}}} \end{matrix}$

where h_(i) ^((t)) represents the current history of a server i at time step t computed recursively via the previous measured power consumption of the server ρ_(i) ^((t−1)), the previous capping limit c_(i) ^((t−1)) of the server, and the previous history h_(i) ^((t−1)), where {circumflex over (P)}(d_(i) ^((t−1))|h_(i) ^((t−1))) is the power demand estimation computed during the previous time step (t-1) by determining/computing a probability distribution of the power demand of the server and a previous server history i.e., the power demand of the server estimated from the Bayesian Theorem, and where {circumflex over (P)}(d_(i) ^((t))|h_(i) ^((t))) is the estimated power demand of the server which is then used for solving the power capping limits of the servers via a hill-climbing method discussed later. In one embodiment, the previous power consumption ρ_(i) ^((t−1)) of the server represents the power consumption of the server when the processor(s) of the server executes a throttle. In one embodiment, a processor throttles when the power demand of the server housing the processor exceeds the power capping limit.

Referring back to FIG. 1, at block 105 power capping limits are computed for each server of the computer system, such as a server rack. In one embodiment, the power capping limits are computed by solving an optimization model based on the estimated/computed probability distribution of the power demand. The optimization model, in one embodiment, is mathematically expressed as:

${\Delta \; {{Loss}_{i}^{(t)}\left( c_{i}^{(t)} \right)}} = {{{{Loss}_{i}^{(t)}\left( c_{i}^{(t)} \right)} - {{Loss}_{i}^{(t)}\left( {c_{i}^{(t)} + 1} \right)}} = {\sum\limits_{d_{i}^{(t)} = {c_{i}^{(t)} + 1}}^{c_{i,\max}}\; {P\left( {D_{i}^{(t)} = d_{i}^{(t)}} \right)}}}$

where Loss_(i) ^((t)) represents performance loss of a server i at time t.

In one embodiment, a hill-climbing method is implemented on a processor for solving the optimization model. The hill-climbing method stops solving the optimization model once an optimum solution with respect to the constraints is reached. In one embodiment, the constraints include a group of servers in the form of a tree hierarchy. The tree hierarchy, in one embodiment, includes data centers with rows of racks and rooms to store the racks. In one embodiment, the time complexity of the hill-climbing method is big O(n log(n)). The hill-climbing method, in one embodiment, is implemented for execution on a processor with the following pseudo-code.

Initialize c_(i) ^((t)) ← c_(i,min), i = 1, . . . , n Loop  I ←   For each server i, if increasing c_(i) ^((t)) does not violate  any constraint, then I ← I ∪ {i}  If I = , return c^((t)) = (c₁ ^((t)), . . . , c_(n) ^((t)))   $\left. i^{*}\leftarrow{\arg \; {\max\limits_{i}{\sum\limits_{d_{i}^{(t)} = {c_{i}^{(t)} + 1}}^{c_{i,{m\; {ax}}}}{\hat{P}\left( d_{i}^{(t)} \middle| h_{i}^{(t)} \right)}}}} \right.$  c_(i*) ^((t)) ← c_(i*) ^((t)) + 1 End Loop

At block 106, the computed power capping limits c_(i*) ^((t)) are dynamically allocated to each server of the computer system. In one embodiment, power controller(s) of each server (see FIG. 5) dynamically allocate and/or enforce the new power capping limits for each server in the computer system. In one embodiment, the sum of the dynamically allocated power capping limits for each server in the computer system is not more than the total power limit of the computer system determined at block 101.

FIG. 2A is a box graph 200 illustrating power demand for servers in a server rack before applying the dynamically allocated power capping limits to the servers, according to one embodiment of the invention. The x-axis represents servers (1, . . . N) while the y-axis represents power consumption in Watts. Each box represents power consumption with respect to a power limit of the server rack. This power limit in FIG. 2A is shown by the dashed line which is the total power limit divided by N. The shaded region of the box below the dashed power limit line is the unused power for a particular server. The unused power region represents an underutilized server given its workload at time t. This means that such a server can take on more work than its current workload. Servers 1, 3, and N are all examples of underutilized servers. Server 2, however, is fully utilized and suffers from a performance loss. The shaded region above the dashed power limit line represents performance loss—power the server would have consumed executing an application had there been no power capping limit.

FIG. 2B is a box graph 210 illustrating power demand for servers in a server rack after applying the dynamically allocated power capping limits to the servers, according to one embodiment of the invention. The x-axis represents servers (1, . . . N) while the y-axis represents power consumption in Watts. In this example, the dynamic power allocator, that performs the method discussed in reference to FIG. 1, dynamically allocates new power capping limits for each server in the rack according to its power demand. Based on the power demand of the servers in FIG. 2A, the new power capping limits are dynamically allocated for the servers as shown in FIG. 2B. Performance loss is reduced (in this example to zero as compared to server 2 in FIG. 2A) for server 2 by allocating a higher power capping limit while lowering the power capping limits for servers 1, 3, and N.

FIG. 3 is a table illustrating the relative reduction in performance loss via the dynamic power allocator, according to one embodiment of the invention. In this example, two power management systems are compared. The first system is a static system in which each server in the rack is provided a fixed power capping limit regardless of the workloads of the server. The second power management system is the dynamic power allocator described in various embodiments herein. The first system is used as a base reference for the dynamic power allocator. In this embodiment, a diverse set of workloads is provided to a rack (computer system) of servers and performance loss for each server in the rack is computed.

In this embodiment, the performance loss of the second system based on the dynamic allocator is 60.8% reduced as compared to the performance loss of the first system based on the static power allocator. The relatively lower performance loss with the dynamic allocator is because the dynamic allocator is able to compute and allocate custom power capping limits regularly for each server based on the diverse workload of each server.

FIG. 4 illustrates an apparatus 400 having a server rack 401 coupled with the dynamic power allocator 403, according to one embodiment of the invention. In one embodiment, the server rack 401 includes one or more servers 405 _(1-N). The server rack, in one embodiment, has a power consumption limit based on the power supply 404, temperature cooling system (not shown), and number of servers 405 _(1-N). In one embodiment, the dynamic power allocator 403 is executed by a processor 402. In one embodiment, the processor 402 is coupled with the server rack 403 via a communication network 406.

The dynamic power allocator 403, in one embodiment, computes power capping limits for each of the servers 405 _(1-N) at every time step as illustrated by the flowchart of FIG. 1. The time step t is configurable by a user or a machine (hardware and/or software) as shown by 407.

FIG. 5 illustrates an apparatus 500 for dynamically allocating power capping limit to a server 501, according to one embodiment of the invention. In one embodiment, the server 501 is coupled with a processor 502 having instructions and logic 503 to execute the dynamic power allocation flowchart of FIG. 1. The server 501, in one embodiment, includes CPU(s) 504 coupled with a power controller 505 and memory 506. In one embodiment, the power capping limit for the server is set by the power controller 505. The power controller 505, in one embodiment, provides the dynamic power allocator 503 with measured power consumption of the server 501. In one embodiment, once the dynamic power allocator 503 computes the new power capping limits for the server, it communicates those new power capping limits to the power controller 501. The server 501 then operates under the new dynamically allocated power capping limits that provide reduced performance loss and more computational capability.

Elements of embodiments are also provided as a machine-readable medium (also referred to as computer readable medium) for storing the computer-executable instructions (e.g., the dynamic power allocator of FIG. 1). The machine-readable medium may include, but is not limited to, flash memory, optical disks, CD-ROMs, DVD ROMs, RAMs, EPROMs, EEPROMs, magnetic or optical cards, or other type of machine-readable media suitable for storing electronic or computer-executable instructions. For example, embodiments of the invention may be downloaded as a computer program (e.g., BIOS) which may be transferred from a remote computer (e.g., a server) to a requesting computer (e.g., a client) by way of data signals via a communication link (e.g., a modem or network connection).

While the invention has been described in conjunction with specific embodiments thereof, many alternatives, modifications and variations will be apparent to those of ordinary skill in the art in light of the foregoing description.

For example, in one embodiment, after the probability distribution of power demand is estimated/computed, an exhaustive search in the space of c^((i))=(c_(i) ^((t)), . . . , c_(i) ^((t)) may be used to solve the optimization model to determine optimal power capping limits for the servers in the server rack. Embodiments of the invention are intended to embrace all such alternatives, modifications, and variations as to fall within the broad scope of the appended claims. 

1-20. (canceled)
 21. A method for allocating power capping limits for first and second servers using foreseeable power demand, the method comprising: estimating a first performance loss for the first server; estimating a second performance loss for the second server; computing first and second power capping limits for the first and second servers respectively, the computing based on a difference between the first performance loss and the second performance loss respectively; and dynamically allocating the first and second power capping limits to the first and second servers respectively, by modifying previous power capping limits of the first and second servers.
 22. The method of claim 21, wherein estimating the first performance loss is estimated by applying estimated probability distribution of power demand for the first server, and wherein estimating the second performance loss is estimated by applying estimated probability distribution of power demand for the second server.
 23. The method of claim 22, wherein the estimated probability distribution of power demand for each of the first and second servers comprises: applying a demand bigram model; applying a power capping model; and applying power consumption history of the respective server.
 24. The method of claim 22, wherein computing each of the first and second power capping limits comprises: solving an optimization model according to the estimated probability distribution of power demand for the respective server.
 25. The method of claim 24, wherein solving the optimization model comprises applying one of: a hill-climbing method; or an exhaustive search in a defined space.
 26. The method of claim 21, wherein dynamically allocating the first and second power capping limits to the first and second servers respectively is performed by first and second power controllers respectively.
 27. The method of claim 21, wherein the first and second servers are positioned in a server rack.
 28. A computer readable storage medium having executable instructions stored thereon that when executed cause a processor to perform a method for allocating power capping limits for first and second servers using foreseeable power demand, the method comprising: estimating a first performance loss for the first server; estimating a second performance loss for the second server; computing first and second power capping limits for the first and second servers respectively, the computing based on a difference between the first performance loss and the second performance loss respectively; and dynamically allocating the first and second power capping limits to the first and second servers respectively, by modifying previous power capping limits of the first and second servers.
 29. The computer readable storage medium of claim 28, wherein estimating the first performance loss is estimated by applying estimated probability distribution of power demand for the first server, and wherein estimating the second performance loss is estimated by applying estimated probability distribution of power demand for the second server.
 30. The computer readable storage medium of claim 29, wherein the estimated probability distribution of power demand for each of the first and second servers comprises: applying a demand bigram model; applying a power capping model; and applying power consumption history of the respective server.
 31. The computer readable storage medium of claim 29, wherein computing each of the first and second power capping limits comprises: solving an optimization model according to the estimated probability distribution of power demand for the respective server.
 32. The computer readable storage medium of claim 31, wherein solving the optimization model comprises applying one of: a hill-climbing method; or an exhaustive search in a defined space.
 33. The computer readable storage medium of claim 28, wherein dynamically allocating the first and second power capping limits to the first and second servers respectively is performed by first and second power controllers respectively.
 34. The computer readable storage medium of claim 28, wherein the first and second servers are positioned in a server rack.
 35. A system comprising: a server rack having first and second servers; a power source to supply power to the first and second servers of the server rack; and a processor, coupled to the server rack, for allocating power capping limits for the first and second servers using foreseeable power demand, the processor operable to: estimate a first performance loss for the first server; estimate a second performance loss for the second server; compute first and second power capping limits for the first and second servers respectively, wherein the first and second power capping limits are computed according to a difference between the first performance loss and the second performance loss respectively; and dynamically allocate the first and second power capping limits to the first and second servers respectively, by modifying previous power capping limits of the first and second servers.
 36. The system of claim 35, wherein the processor to estimate the first performance loss by applying estimated probability distribution of power demand for the first server, and wherein the processor to estimate the second performance loss by applying estimated probability distribution of power demand for the second server.
 37. The system of claim 36, wherein the processor to estimate the probability distribution of power demand for each of the first and second servers by: applying a demand bigram model; applying a power capping model; and applying power consumption history of the respective server.
 38. The system of claim 36, wherein the processor to compute each of the first and second power capping limits by: solving an optimization model according to the estimated probability distribution of power demand for the respective server.
 39. The system of claim 38, wherein the processor to solve the optimization model by applying one of: a hill-climbing method; or an exhaustive search in a defined space.
 40. The system of claim 36, wherein the processor to dynamically allocate the first and second power capping limits to the first and second servers respectively by first and second power controllers respectively. 